Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "187" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 38 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 36 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459853 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.194833 | -0.538085 | 0.638490 | -0.923812 | -0.950113 | -0.700595 | 3.382978 | 3.587845 | 0.7380 | 0.6989 | 0.4277 | 1.674471 | 1.389969 |
| 2459852 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 8.65% | 8.11% | -0.570610 | 0.407129 | 0.351554 | -0.914421 | -0.686703 | -0.469227 | 0.266073 | -0.158103 | 0.8328 | 0.8373 | 0.2352 | 3.752346 | 3.124104 |
| 2459851 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 11.76% | 0.00% | 0.056003 | 0.057342 | 0.716017 | -0.758324 | 0.326689 | 0.698248 | 2.683066 | 2.550073 | 0.7546 | 0.7384 | 0.3389 | 1.900895 | 1.533786 |
| 2459850 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.665180 | 0.121405 | 0.651691 | -0.776529 | -0.800672 | 0.100756 | 2.991364 | 5.207422 | 0.7421 | 0.7640 | 0.3498 | 3.229130 | 2.887013 |
| 2459849 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 16.67% | 0.00% | 0.375848 | -0.355098 | 1.384342 | -0.270494 | -0.786248 | -0.436191 | 3.885663 | 2.584472 | 0.7406 | 0.7543 | 0.3565 | 1.484158 | 1.270533 |
| 2459848 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 30.15% | 0.00% | 0.714023 | 0.335943 | -0.433443 | -0.454420 | -0.665961 | -0.305508 | 3.413614 | 2.764934 | 0.7217 | 0.7586 | 0.3749 | 1.478628 | 1.265674 |
| 2459847 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 3.74% | 1.07% | 0.826565 | 0.740494 | -0.328296 | -0.537064 | -0.619650 | -0.114381 | 2.943955 | 3.024400 | 0.7209 | 0.6920 | 0.4312 | 1.649014 | 1.358014 |
| 2459846 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 33.33% | 0.00% | 1.042534 | 0.878134 | 0.077012 | -0.119089 | -0.358275 | -0.804768 | 1.525157 | 1.195103 | 0.8254 | 0.6704 | 0.4971 | 1.379311 | 1.208199 |
| 2459845 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.224756 | 0.547321 | -0.042945 | 1.127616 | 0.071905 | -0.408674 | 6.856228 | 1.109678 | 0.7350 | 0.7479 | 0.3719 | 6.421176 | 5.955468 |
| 2459844 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 0.029899 | -0.239057 | -0.505419 | -1.024760 | 1.664224 | -0.209815 | 2.220132 | 0.196434 | 0.0248 | 0.0245 | 0.0004 | nan | nan |
| 2459843 | digital_ok | 100.00% | 1.20% | 0.66% | 0.00% | 100.00% | 0.00% | 0.463158 | -0.328545 | -0.562279 | -0.766029 | 0.151600 | -0.851485 | 6.161788 | 2.400497 | 0.7417 | 0.7449 | 0.3959 | 4.619231 | 3.378136 |
| 2459842 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.373964 | -0.575181 | 0.367471 | 0.141161 | 0.367983 | 0.268237 | 4.094203 | 0.770797 | 0.7518 | 0.6899 | 0.2463 | 4.754292 | 6.017473 |
| 2459841 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.679238 | -0.595340 | -1.066314 | -0.788650 | 0.007276 | -1.101395 | 1.523813 | -0.034322 | 0.0250 | 0.0244 | 0.0007 | nan | nan |
| 2459840 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 168.883330 | 237.996824 | 93.713100 | 83.561292 | 1352.485190 | 1057.726833 | 2884.468947 | 2625.130231 | 0.0170 | 0.0162 | 0.0007 | nan | nan |
| 2459839 | digital_ok | 100.00% | - | - | - | - | - | -0.691922 | 0.223165 | -0.898532 | -0.821282 | 5.069504 | 6.169024 | 2.914179 | 0.100222 | nan | nan | nan | nan | nan |
| 2459838 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.148167 | 0.524422 | -0.096341 | -0.122617 | 0.489409 | 0.336836 | 1.643978 | 1.698244 | 0.7384 | 0.7176 | 0.4093 | 0.000000 | 0.000000 |
| 2459835 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.758458 | 0.108011 | 0.268574 | 0.033760 | 0.204464 | -0.276997 | 1.529936 | 1.118867 | 0.0359 | 0.0383 | 0.0056 | nan | nan |
| 2459833 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.767552 | -0.196230 | -0.532523 | -0.875659 | -0.306834 | -0.204300 | 2.233261 | 0.858503 | 0.0269 | 0.0288 | 0.0015 | nan | nan |
| 2459832 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 7.89% | -0.252834 | 0.029844 | -0.375923 | -0.019299 | -0.554038 | -0.630408 | 3.264376 | 1.574769 | 0.8035 | 0.5349 | 0.5838 | 1.934541 | 1.516537 |
| 2459831 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.447303 | 0.558324 | -0.671519 | -0.681243 | 2.042861 | 3.713890 | 1.366426 | -0.563902 | 0.0265 | 0.0278 | 0.0013 | nan | nan |
| 2459830 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 5.26% | -0.192252 | 1.156133 | 0.017743 | 0.371122 | 0.305230 | -0.045576 | 3.990341 | 1.887602 | 0.8009 | 0.5401 | 0.5747 | 2.213438 | 1.468190 |
| 2459829 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.217369 | 2.141575 | 0.314203 | 0.502404 | -0.259736 | -1.009658 | 7.222698 | 3.435065 | 0.7467 | 0.6725 | 0.4228 | 0.000000 | 0.000000 |
| 2459828 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.165804 | 1.213419 | 0.235383 | 0.338708 | -0.166333 | -1.056257 | 5.816909 | 2.382474 | 0.7981 | 0.5564 | 0.5415 | 8.839093 | 3.623260 |
| 2459827 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.511196 | 1.635680 | 0.215445 | 1.292029 | -0.530180 | -0.561925 | 6.194864 | 2.330439 | 0.7608 | 0.6905 | 0.4123 | 8.224500 | 6.913602 |
| 2459826 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.018049 | 1.212439 | 0.234772 | 1.158764 | -0.053832 | -0.823318 | 4.441230 | -0.625749 | 0.8000 | 0.5919 | 0.5017 | 6.167923 | 3.751802 |
| 2459825 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.173013 | 0.868696 | 0.177305 | -0.001760 | 4.892599 | 4.474083 | 5.792006 | 3.773466 | 0.7984 | 0.5965 | 0.5072 | 0.000000 | 0.000000 |
| 2459824 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 92.00% | 0.00% | 0.723528 | 1.216827 | 0.311757 | 0.052243 | 0.415574 | -0.205876 | 3.436101 | 1.894368 | 0.7163 | 0.7489 | 0.3695 | 0.000000 | 0.000000 |
| 2459823 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.051655 | 0.625112 | 0.437129 | 0.912772 | 0.482460 | -0.894384 | 15.354463 | 2.553835 | 0.7559 | 0.6563 | 0.4613 | 0.000000 | 0.000000 |
| 2459822 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.989075 | 1.076690 | 0.349241 | 0.841475 | -0.008652 | -0.991489 | 7.876410 | 0.684035 | 0.8014 | 0.6199 | 0.4957 | 0.000000 | 0.000000 |
| 2459821 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 5.26% | 7.89% | 1.206200 | 1.435063 | 0.144469 | 1.134729 | 0.476210 | -0.111952 | 3.291840 | -0.171370 | 0.7857 | 0.6144 | 0.5037 | 1.238275 | 1.042021 |
| 2459820 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 50.80% | 0.048535 | 1.389039 | -0.033376 | 1.058574 | -1.182497 | -1.914160 | 1.182373 | -1.019388 | 0.7721 | 0.6907 | 0.4163 | 0.000000 | 0.000000 |
| 2459817 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 78.95% | 0.480557 | 0.853510 | 0.064601 | 0.666919 | 0.433169 | -0.730204 | 1.394497 | 0.836512 | 0.7910 | 0.6458 | 0.5012 | 0.000000 | 0.000000 |
| 2459816 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 27.91% | 0.996786 | 0.487764 | 0.337566 | 0.364200 | 0.519668 | -1.038951 | 2.917940 | 2.509910 | 0.8389 | 0.6025 | 0.5767 | 0.131733 | 0.130450 |
| 2459815 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 47.37% | 0.605913 | 0.958179 | 0.418816 | 0.399341 | -0.117494 | -1.534025 | 1.727269 | 1.024652 | 0.7755 | 0.6432 | 0.5115 | 0.788450 | 0.769167 |
| 2459814 | digital_ok | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.839061 | 2.404922 | -0.103757 | 0.807094 | -0.715842 | -1.651003 | 5.798421 | 2.729755 | 0.7713 | 0.6944 | 0.4242 | 0.000000 | 0.000000 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | nn Temporal Discontinuties | 3.587845 | -0.538085 | 0.194833 | -0.923812 | 0.638490 | -0.700595 | -0.950113 | 3.587845 | 3.382978 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | nn Shape | 0.407129 | -0.570610 | 0.407129 | 0.351554 | -0.914421 | -0.686703 | -0.469227 | 0.266073 | -0.158103 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | ee Temporal Discontinuties | 2.683066 | 0.056003 | 0.057342 | 0.716017 | -0.758324 | 0.326689 | 0.698248 | 2.683066 | 2.550073 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | nn Temporal Discontinuties | 5.207422 | 0.665180 | 0.121405 | 0.651691 | -0.776529 | -0.800672 | 0.100756 | 2.991364 | 5.207422 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | ee Temporal Discontinuties | 3.885663 | 0.375848 | -0.355098 | 1.384342 | -0.270494 | -0.786248 | -0.436191 | 3.885663 | 2.584472 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | ee Temporal Discontinuties | 3.413614 | 0.335943 | 0.714023 | -0.454420 | -0.433443 | -0.305508 | -0.665961 | 2.764934 | 3.413614 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | nn Temporal Discontinuties | 3.024400 | 0.740494 | 0.826565 | -0.537064 | -0.328296 | -0.114381 | -0.619650 | 3.024400 | 2.943955 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | ee Temporal Discontinuties | 1.525157 | 1.042534 | 0.878134 | 0.077012 | -0.119089 | -0.358275 | -0.804768 | 1.525157 | 1.195103 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | ee Temporal Discontinuties | 6.856228 | 0.547321 | 1.224756 | 1.127616 | -0.042945 | -0.408674 | 0.071905 | 1.109678 | 6.856228 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | ee Temporal Discontinuties | 2.220132 | 0.029899 | -0.239057 | -0.505419 | -1.024760 | 1.664224 | -0.209815 | 2.220132 | 0.196434 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | ee Temporal Discontinuties | 6.161788 | -0.328545 | 0.463158 | -0.766029 | -0.562279 | -0.851485 | 0.151600 | 2.400497 | 6.161788 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | ee Temporal Discontinuties | 4.094203 | -0.373964 | -0.575181 | 0.367471 | 0.141161 | 0.367983 | 0.268237 | 4.094203 | 0.770797 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | ee Temporal Discontinuties | 1.523813 | -0.679238 | -0.595340 | -1.066314 | -0.788650 | 0.007276 | -1.101395 | 1.523813 | -0.034322 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | ee Temporal Discontinuties | 2884.468947 | 168.883330 | 237.996824 | 93.713100 | 83.561292 | 1352.485190 | 1057.726833 | 2884.468947 | 2625.130231 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | nn Temporal Variability | 6.169024 | 0.223165 | -0.691922 | -0.821282 | -0.898532 | 6.169024 | 5.069504 | 0.100222 | 2.914179 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | nn Temporal Discontinuties | 1.698244 | 0.524422 | 0.148167 | -0.122617 | -0.096341 | 0.336836 | 0.489409 | 1.698244 | 1.643978 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | ee Temporal Discontinuties | 1.529936 | 0.108011 | -0.758458 | 0.033760 | 0.268574 | -0.276997 | 0.204464 | 1.118867 | 1.529936 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | ee Temporal Discontinuties | 2.233261 | -0.196230 | -0.767552 | -0.875659 | -0.532523 | -0.204300 | -0.306834 | 0.858503 | 2.233261 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | ee Temporal Discontinuties | 3.264376 | -0.252834 | 0.029844 | -0.375923 | -0.019299 | -0.554038 | -0.630408 | 3.264376 | 1.574769 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | nn Temporal Variability | 3.713890 | -0.447303 | 0.558324 | -0.671519 | -0.681243 | 2.042861 | 3.713890 | 1.366426 | -0.563902 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | ee Temporal Discontinuties | 3.990341 | -0.192252 | 1.156133 | 0.017743 | 0.371122 | 0.305230 | -0.045576 | 3.990341 | 1.887602 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | ee Temporal Discontinuties | 7.222698 | 2.141575 | 1.217369 | 0.502404 | 0.314203 | -1.009658 | -0.259736 | 3.435065 | 7.222698 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | ee Temporal Discontinuties | 5.816909 | 1.213419 | 1.165804 | 0.338708 | 0.235383 | -1.056257 | -0.166333 | 2.382474 | 5.816909 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | ee Temporal Discontinuties | 6.194864 | 0.511196 | 1.635680 | 0.215445 | 1.292029 | -0.530180 | -0.561925 | 6.194864 | 2.330439 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | ee Temporal Discontinuties | 4.441230 | 1.212439 | 1.018049 | 1.158764 | 0.234772 | -0.823318 | -0.053832 | -0.625749 | 4.441230 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | ee Temporal Discontinuties | 5.792006 | 0.868696 | 0.173013 | -0.001760 | 0.177305 | 4.474083 | 4.892599 | 3.773466 | 5.792006 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | ee Temporal Discontinuties | 3.436101 | 0.723528 | 1.216827 | 0.311757 | 0.052243 | 0.415574 | -0.205876 | 3.436101 | 1.894368 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | ee Temporal Discontinuties | 15.354463 | 0.625112 | 1.051655 | 0.912772 | 0.437129 | -0.894384 | 0.482460 | 2.553835 | 15.354463 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | ee Temporal Discontinuties | 7.876410 | 0.989075 | 1.076690 | 0.349241 | 0.841475 | -0.008652 | -0.991489 | 7.876410 | 0.684035 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | ee Temporal Discontinuties | 3.291840 | 1.435063 | 1.206200 | 1.134729 | 0.144469 | -0.111952 | 0.476210 | -0.171370 | 3.291840 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | nn Shape | 1.389039 | 0.048535 | 1.389039 | -0.033376 | 1.058574 | -1.182497 | -1.914160 | 1.182373 | -1.019388 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | ee Temporal Discontinuties | 1.394497 | 0.480557 | 0.853510 | 0.064601 | 0.666919 | 0.433169 | -0.730204 | 1.394497 | 0.836512 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | ee Temporal Discontinuties | 2.917940 | 0.487764 | 0.996786 | 0.364200 | 0.337566 | -1.038951 | 0.519668 | 2.509910 | 2.917940 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | ee Temporal Discontinuties | 1.727269 | 0.958179 | 0.605913 | 0.399341 | 0.418816 | -1.534025 | -0.117494 | 1.024652 | 1.727269 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 187 | N14 | digital_ok | ee Temporal Discontinuties | 5.798421 | 2.404922 | 0.839061 | 0.807094 | -0.103757 | -1.651003 | -0.715842 | 2.729755 | 5.798421 |